Backpropagation
Version 1.0.0 · Updated 2026-07-30
CORE DEFINITION
The core algorithm for training neural networks. First, forward propagation produces the output, then the error between the output and the true value is calculated, and this error is then "backpropagated" through the network, adjusting the weights of each layer's neurons proportionally to reduce the next error. Scaffolding role: feedback correction mechanism. This is the essence of efficient learning. Do not only make "forward" efforts (working hard), but also have "backward" error propagation (review). Precisely identify which link (weight) caused the final failure, and make targeted adjustments rather than blindly starting over.
SCAFFOLDING EFFECT
Reduce cognitive load
Feedback correction mechanism. This is the essence of efficient learning. Do not only make "forward" efforts (working hard), but also have "backward" error propagation (review). Precisely identify which link (weight) caused the final failure, and make targeted adjustments rather than blindly starting over.
Anchor fast decisions
Application of the chain rule: forward propagation computes the output and loss, then the gradient of the loss with respect to each weight (∂L/∂w) is computed layer by layer in reverse, and weights are adjusted via gradient descent to reduce the error. The core is "error attribution to each layer," enabling deep networks to be trainable. Analogy: first total error, then allocate responsibility to each component.
MINIMUM ACTION
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Backpropagationverified
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